DAWNBench is a benchmark suite for end-to-end deep learning training and inference.
Computation time and cost are critical resources in building deep models, yet many existing benchmarks focus solely on model accuracy.
DAWNBench provides a reference set of common deep learning workloads for quantifying training time, training cost, inference latency, and inference cost across different optimization strategies, model architectures, software frameworks, clouds, and hardware.

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DAWNBench is part of a larger community conversation about the future of machine learning infrastructure. Sound off on the DAWNBench google group.

Disclosure: The Stanford DAWN research project is a five-year industrial affiliates program at Stanford University and is financially supported in part by founding members including Intel, Microsoft, NEC, Teradata, VMWare, and Google.
For more information, including information regarding Stanford’s policies on openness in research and policies affecting industrial affiliates program membership, please see DAWN's membership page.